Analysis CLL Proteomics - Dimension Reduction
Sophie Rabe
Analysis date: 2021-03-29
Setup
Load libraries
library(plyr)
library(gtools)
library(pheatmap)
library(Matrix)
library(Hmisc)
library(ggpubr)
library(ggbeeswarm)
library(DESeq2)
library(tidyverse)
library(vsn)
library(fdrtool)
library(limma)
library(Rtsne)
library(MultiAssayExperiment)
library(gplots)
library(viridis)Load data
source("data/Figure_layouts.R")
load("data/CLL_Proteomics_Setup.RData")
load("data/CLL_Proteomics_ConsensusClustering.RData")
load("data/GOterm_BCR.RData")
load("data/GOterm_Spliceosome.RData")new_colData <- left_join(colData(multiomics_MAE) %>% as_tibble(),
enframe(CCP_group5, value = "PG", name = "patient_ID") ) ## Joining, by = "patient_ID"new_colData <- left_join(new_colData,
enframe(CCP_group6_RNA, value = "CCP6_RNA", name = "patient_ID") ) %>%
as.data.frame() %>%
mutate(rn = patient_ID) %>%
column_to_rownames(., var = "rn")## Joining, by = "patient_ID"colData(multiomics_MAE)$PG <- as.factor(new_colData$PG)
colData(multiomics_MAE)$CCP6_RNA <- as.factor(new_colData$CCP6_RNA)Analysis
Dimension reduction plots
tsne (Barnes-Hut-SNE)
t-SNE colored by genetic alterations and groups
set.seed(10)
rtsne_out <- Rtsne( t(assay(multiomics_MAE[prot_few_nas , ,"proteomics"])), perplexity = 10 )
rtsne_out_df <- rtsne_out$Y %>% as.data.frame() %>% as_tibble()
rtsne_out_df$pat_ID <- colnames(assay(multiomics_MAE[prot_few_nas , ,"proteomics"]))
rtsne_out_df <- left_join(rtsne_out_df,
wideFormat(multiomics_MAE[, ,c("SNPs","chrom_abber", "health_record_bin") ], colDataCols = c("gender", "treatment_status", "doehner_groups", "PG")) %>% as_tibble(),
by=c("pat_ID"="primary") )
rtsne_out_df <- mutate_at(rtsne_out_df, colnames(rtsne_out_df %>% dplyr::select(SNPs_ATM:health_record_bin_treated)), as.logical)
#rtsne_out_df <- rtsne_out_df %>% replace(is.na(.), "unknown")
message("t-SNE colored by consensus cluster groups")
tsne_CCP_P_plot <- ggplot(rtsne_out_df, aes(V1, V2)) +
geom_point(aes(fill=PG), color="grey", shape=21) +
scale_fill_manual(values = colors_CCP)+
pp_sra +
guides(color=guide_legend(title="PG"))
tsne_CCP_P_plot+ theme(aspect.ratio=1, plot.title = element_text(size = 30))
message("t-SNE colored by combination of IGHV status and trisomy12")
tsne_IGHVtris_plot <- ggplot(rtsne_out_df %>% filter(health_record_bin_IGHV_mutated!="unknown", chrom_abber_trisomy12!="unknown"), aes(V1, V2)) +
geom_point(aes(fill=interaction(health_record_bin_IGHV_mutated, chrom_abber_trisomy12)), color="grey", shape=21) +
scale_fill_manual(values = colors_CCP[c(1,3,4,2)] )+
pp_sra_noguides
tsne_IGHVtris_plot + theme(aspect.ratio=1, plot.title = element_text(size = 30)) #+
sapply(colnames(rtsne_out_df)[c(4,5,8:26)], function(var){
print(ggplot(rtsne_out_df, aes(V1, V2)) +
geom_point(aes_string(color=var), size=3) +
scale_color_manual(values = c( "#0571b0", "#ca0020", "grey")) +
ggtitle(gsub("SNPs_", "", gsub("chrom_abber_", "", gsub( "health_record_bin_", "", var) ))) +
pp_sra_noguides +
theme(aspect.ratio=1, plot.title = element_text(size = 30)) )
})
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 13 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 29 rows containing missing values (geom_point).
## gender treatment_status SNPs_ATM SNPs_BIRC3 SNPs_EGR2 SNPs_NOTCH1
## data List,26 List,26 List,26 List,26 List,26 List,26
## layers List,1 List,1 List,1 List,1 List,1 List,1
## scales ? ? ? ? ? ?
## mapping List,2 List,2 List,2 List,2 List,2 List,2
## theme List,93 List,93 List,93 List,93 List,93 List,93
## coordinates ? ? ? ? ? ?
## facet ? ? ? ? ? ?
## plot_env ? ? ? ? ? ?
## labels List,4 List,4 List,4 List,4 List,4 List,4
## guides List,4 List,4 List,4 List,4 List,4 List,4
## SNPs_POT1 SNPs_SF3B1 SNPs_TP53 SNPs_XPO1 chrom_abber_del11q
## data List,26 List,26 List,26 List,26 List,26
## layers List,1 List,1 List,1 List,1 List,1
## scales ? ? ? ? ?
## mapping List,2 List,2 List,2 List,2 List,2
## theme List,93 List,93 List,93 List,93 List,93
## coordinates ? ? ? ? ?
## facet ? ? ? ? ?
## plot_env ? ? ? ? ?
## labels List,4 List,4 List,4 List,4 List,4
## guides List,4 List,4 List,4 List,4 List,4
## chrom_abber_del13q14 chrom_abber_del17p13 chrom_abber_gain8q24
## data List,26 List,26 List,26
## layers List,1 List,1 List,1
## scales ? ? ?
## mapping List,2 List,2 List,2
## theme List,93 List,93 List,93
## coordinates ? ? ?
## facet ? ? ?
## plot_env ? ? ?
## labels List,4 List,4 List,4
## guides List,4 List,4 List,4
## chrom_abber_trisomy12 health_record_bin_elderly_at_diagnosis
## data List,26 List,26
## layers List,1 List,1
## scales ? ?
## mapping List,2 List,2
## theme List,93 List,93
## coordinates ? ?
## facet ? ?
## plot_env ? ?
## labels List,4 List,4
## guides List,4 List,4
## health_record_bin_elderly_patient health_record_bin_gender_binary
## data List,26 List,26
## layers List,1 List,1
## scales ? ?
## mapping List,2 List,2
## theme List,93 List,93
## coordinates ? ?
## facet ? ?
## plot_env ? ?
## labels List,4 List,4
## guides List,4 List,4
## health_record_bin_IGHV_mutated
## data List,26
## layers List,1
## scales ?
## mapping List,2
## theme List,93
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
## health_record_bin_komplex_abberant_karyotype
## data List,26
## layers List,1
## scales ?
## mapping List,2
## theme List,93
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
## health_record_bin_treated
## data List,26
## layers List,1
## scales ?
## mapping List,2
## theme List,93
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4message("There is one trisomy12 negative patient which clusters with all of the other trisomy12 patients. Does it have a subclonal mutations?")## There is one trisomy12 negative patient which clusters with all of the other trisomy12 patients. Does it have a subclonal mutations?metadata(multiomics_MAE)$fish_df_clonsizes["trisomy12" ,] %>%
dplyr::select(rtsne_out_df %>% filter(chrom_abber_trisomy12==FALSE) %>% arrange(desc(V1)) %>% slice(1:2) %>% .$pat_ID)BCR signaling
Boxplots mean abundance
BCR_genes_mean <- assay(multiomics_MAE[BCR_genes, ,"proteomics"]) %>% colMeans(na.rm = TRUE) %>% enframe()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'left_join(BCR_genes_mean,
wideFormat(multiomics_MAE[ c("IGHV_mutated", "trisomy12"), ,]) %>% as_tibble(),
by=c("name"="primary")) %>%
mutate_at(.vars =c("chrom_abber_trisomy12", "health_record_bin_IGHV_mutated" ), as.logical ) %>%
filter(!is.na(chrom_abber_trisomy12)) %>%
ggplot(aes( chrom_abber_trisomy12, value ))+
geom_boxplot(aes(fill=chrom_abber_trisomy12)) + geom_beeswarm() +
stat_compare_means(method = "t.test", label = "p.signif", comparisons = list(c("TRUE", "FALSE"))) +
ggtitle("BCR protein abundance") +
scale_fill_manual(values=c("#0571b0", "#ca0020")) +
pp_sra_nox +
ylab("Mean abundance of BCR proteins")+
theme(aspect.ratio=2) +
theme(legend.position = "bottom")## harmonizing input:
## removing 428 sampleMap rows not in names(experiments)
left_join(BCR_genes_mean,
wideFormat(multiomics_MAE[ c("IGHV_mutated", "trisomy12"), ,]) %>% as_tibble(),
by=c("name"="primary")) %>%
mutate_at(.vars =c("chrom_abber_trisomy12", "health_record_bin_IGHV_mutated" ), as.logical ) %>%
filter(!is.na(health_record_bin_IGHV_mutated)) %>%
ggplot(aes( health_record_bin_IGHV_mutated, value ))+
geom_boxplot(aes(fill=health_record_bin_IGHV_mutated)) + geom_beeswarm() +
stat_compare_means(method = "t.test", label = "p.signif", comparisons = list(c("TRUE", "FALSE"))) +
ggtitle("BCR protein abundance") +
scale_fill_manual(values=c("#0571b0", "#ca0020")) +
pp_sra_nox +
ylab("Mean abundance of BCR proteins")+
theme(aspect.ratio=2) +
theme(legend.position = "bottom")## harmonizing input:
## removing 428 sampleMap rows not in names(experiments)
BCR_CCP_P_plot <- left_join(BCR_genes_mean,
colData(multiomics_MAE) %>% as_tibble() %>% select(name=patient_ID, PG ),
by=c("name")) %>%
filter(!is.na(PG)) %>%
ggplot(aes( PG, value ))+
geom_boxplot(aes(fill=PG)) + geom_beeswarm() +
ggtitle("BCR protein abundance") +
scale_fill_manual(values=colors_CCP) +
pp_sra +
ylab("Mean abundance of BCR proteins")+
#stat_compare_means(label = "p.signif", method = "t.test",
# ref.group = ".all.", label.y = 0.2, hide.ns = TRUE) +
guides(fill=guide_legend(title="PG"))
BCR_CCP_P_plot + theme(aspect.ratio=1) +
stat_compare_means(method = "anova", label.y = 0.22, hjust=0)
Heatmaps
Selected BCR proteins
sel_BCR <- c("ZAP70", "IGHM", "CD79A", "CD79B", "SYK", "PLCG2", "BTK", "PTPN6",
"MAPK1", "PIK3CD", "AKT1", "IKBKB")
tmp_BCR <- wideFormat(multiomics_MAE[sel_BCR, ,"proteomics"], colDataCols = c("PG", "IGHV_mutated" )) %>%
as_tibble()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'tmp_BCR_mx <- tmp_BCR %>% dplyr::select(-primary, -PG, -IGHV_mutated) %>% as.matrix()
rownames(tmp_BCR_mx) <- tmp_BCR$primary
colnames(tmp_BCR_mx) <- gsub("proteomics_", "", colnames(tmp_BCR_mx))
tmp_BCR_anno <- tmp_BCR[, c("PG", "IGHV_mutated")] %>% as.data.frame()
rownames(tmp_BCR_anno) <- tmp_BCR$primary
tmp_BCR_anno$IGHV[tmp_BCR_anno$IGHV_mutated==0] <- "U-CLL"
tmp_BCR_anno$IGHV[tmp_BCR_anno$IGHV_mutated==1] <- "M-CLL"
tmp_BCR_anno <- tmp_BCR_anno %>% select(-IGHV_mutated)
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ),
IGHV=c("U-CLL"= "#0571b0", "M-CLL"= "#ca0020"))
tmp_BCR_anno$PG <- factor(tmp_BCR_anno$PG, levels = c(5,1:4,6))
breaks= seq(min(tmp_BCR_mx), max(tmp_BCR_mx), 0.1)^2
breaks= sort(c(-breaks, breaks))
breaks <- breaks[! (breaks < min(tmp_BCR_mx) | breaks > max(tmp_BCR_mx) )]
pat_order_hclust <- sapply(c(5,1:4,6), function(P){
hc <- hclust(dist(tmp_BCR_mx[rownames(tmp_BCR_anno[tmp_BCR_anno$PG==P,]), ] ))
hc$labels[hc$order]
}) %>% unlist
PG_BCR_proteins_pheatmap <- pheatmap(t(tmp_BCR_mx[pat_order_hclust, ]),
annotation_col = tmp_BCR_anno, annotation_colors = ann_colors, scale = "row", cluster_cols = FALSE,
color = inferno(length(breaks)), border_color = NA,
gaps_col = (which(!tmp_BCR_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$PG %>% duplicated())-1)[-1],
breaks = breaks , cutree_rows = 4, show_colnames = F, treeheight_row = 0, fontsize_row = 8)
All KEGG BCR signaling proteins
tmp_BCR <- wideFormat(multiomics_MAE[BCR_genes, ,"proteomics"], colDataCols = c("PG", "IGHV_mutated" )) %>%
as_tibble()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'tmp_BCR_mx <- tmp_BCR %>% dplyr::select(-primary, -PG, -IGHV_mutated) %>% as.matrix()
rownames(tmp_BCR_mx) <- tmp_BCR$primary
colnames(tmp_BCR_mx) <- gsub("proteomics_", "", colnames(tmp_BCR_mx))
tmp_BCR_anno <- tmp_BCR[, c("PG", "IGHV_mutated")] %>% as.data.frame()
tmp_BCR_anno$PG <- factor(tmp_BCR_anno$PG,levels = c(5,1:4,6)) #
rownames(tmp_BCR_anno) <- tmp_BCR$primary
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ))
message("Heatmap of abundance BCR signaling proteins ordered according to PG")## Heatmap of abundance BCR signaling proteins ordered according to PGpheatmap(tmp_BCR_mx[(tmp_BCR_anno %>% rownames_to_column() %>% arrange(PG, IGHV_mutated) %>% .$rowname), ],
annotation_row = tmp_BCR_anno, annotation_colors = ann_colors, scale = "column", cluster_rows = FALSE, color = inferno(10), border_color = NA,
gaps_row = (which(!tmp_BCR_anno %>% rownames_to_column() %>% arrange(PG, IGHV_mutated) %>% .$PG %>% duplicated())-1)[-1])
Spliceosome
All KEGG proteins
splice_genes_mean <- assay(multiomics_MAE[splice_genes, ,"proteomics"]) %>% colMeans(na.rm = TRUE) %>% enframe()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'splicing_CCP_P_plot <- left_join(splice_genes_mean,
colData(multiomics_MAE) %>% as_tibble() %>% select(name=patient_ID, PG ),
by=c("name")) %>%
filter(!is.na(PG)) %>%
ggplot(aes( PG, value ))+
geom_boxplot(aes(fill=PG)) + geom_beeswarm() +
ggtitle("Spliceosome protein abundance") +
pp_sra +
ylab("Mean abundance of spliceosome proteins")+
#stat_compare_means(label = "p.signif", method = "t.test",
# ref.group = ".all.", label.y = 0.2, hide.ns = TRUE)+
guides(fill=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP)
splicing_CCP_P_plot + theme(aspect.ratio=1) +
stat_compare_means(method = "anova", label.y = 0.22, hjust=0)
tmp_splice <- wideFormat(multiomics_MAE[splice_genes, ,"proteomics"], colDataCols = c("PG", "IGHV_mutated")) %>% as_tibble()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'tmp_splice_mx <- tmp_splice %>% dplyr::select(-primary, -PG, -IGHV_mutated) %>% as.matrix()
rownames(tmp_splice_mx) <- tmp_splice$primary
colnames(tmp_splice_mx) <- gsub("proteomics_", "", colnames(tmp_splice_mx))
tmp_splice_anno <- tmp_splice %>% dplyr::select(PG, IGHV_mutated) %>% as.data.frame()
rownames(tmp_splice_anno) <- tmp_splice$primary
tmp_splice_anno$PG <- factor(tmp_splice_anno$PG,levels = c(5,1:4,6))
message("Heatmap of abundance spliceosome proteins ordered according to PG")## Heatmap of abundance spliceosome proteins ordered according to PGpheatmap(tmp_splice_mx[(tmp_splice_anno %>% rownames_to_column() %>% arrange( PG, IGHV_mutated) %>% .$rowname), ],
annotation_row = tmp_splice_anno, annotation_colors = ann_colors, scale = "column", cluster_rows = FALSE, color = inferno(10), border_color = NA,
gaps_row = (which(!tmp_splice_anno %>% rownames_to_column() %>% arrange(PG, IGHV_mutated) %>% .$PG %>% duplicated())-1)[-1])
Selected spliceosome proteins
some_spliceprots <- c("SF3B1", "SNRPA", "PRPF6", "PRPF3", "SF3A1", "SNRPD2", "SRSF4", "CDC5L", "PRPF19",
"CRNKL1", "PUF60", "PRPF8",
"SNRPB2")
tmp_Splice_GO <- wideFormat(multiomics_MAE[some_spliceprots, ,"proteomics"], colDataCols = c("PG", "IGHV_mutated" )) %>%
as_tibble()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'tmp_Splice_GO_mx <- tmp_Splice_GO %>% dplyr::select(-primary, -PG, -IGHV_mutated) %>% as.matrix()
rownames(tmp_Splice_GO_mx) <- tmp_Splice_GO$primary
colnames(tmp_Splice_GO_mx) <- gsub("proteomics_", "", colnames(tmp_Splice_GO_mx))
tmp_Splice_GO_anno <- tmp_Splice_GO[, c("PG", "IGHV_mutated")] %>% as.data.frame()
rownames(tmp_Splice_GO_anno) <- tmp_Splice_GO$primary
tmp_Splice_GO_anno$PG <- factor(tmp_Splice_GO_anno$PG,levels = c(5,1:4,6))
SF3B1mut <- wideFormat(multiomics_MAE["SF3B1",,"SNPs"])$SNPs_SF3B1## harmonizing input:
## removing 510 sampleMap rows not in names(experiments)
## removing 1 colData rownames not in sampleMap 'primary'names(SF3B1mut) <- wideFormat(multiomics_MAE["SF3B1",,"SNPs"])$primary## harmonizing input:
## removing 510 sampleMap rows not in names(experiments)
## removing 1 colData rownames not in sampleMap 'primary'tmp_Splice_GO_anno$SF3B1 <- SF3B1mut[rownames(tmp_Splice_GO_anno)]
tmp_Splice_GO_anno$SF3B1[tmp_Splice_GO_anno$SF3B1 == 1] <- "mut"
tmp_Splice_GO_anno$SF3B1[tmp_Splice_GO_anno$SF3B1==0] <- "wt"
tmp_Splice_GO_anno$SF3B1 <- as.factor(tmp_Splice_GO_anno$SF3B1)
tmp_Splice_GO_anno$IGHV[tmp_Splice_GO_anno$IGHV_mutated==0] <- "U-CLL"
tmp_Splice_GO_anno$IGHV[tmp_Splice_GO_anno$IGHV_mutated==1] <- "M-CLL"
tmp_Splice_GO_anno <- tmp_Splice_GO_anno %>% select(-IGHV_mutated)
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ),
SF3B1=c("mut"="darkblue", "wt"="gray80"),
IGHV=c("U-CLL"= "#0571b0", "M-CLL"= "#ca0020"))
pat_order_hclust_splice <- sapply(c(5,1:4,6), function(P){
hc <- hclust(dist(tmp_Splice_GO_mx[rownames(tmp_Splice_GO_anno[tmp_Splice_GO_anno$PG==P,]), ] ))
hc$labels[hc$order]
}) %>% unlist
PG_splice_proteins_pheatmap <- pheatmap(t(tmp_Splice_GO_mx[pat_order_hclust_splice, ]),
annotation_col = tmp_Splice_GO_anno, annotation_colors = ann_colors, scale = "row", cluster_cols = FALSE,
color = inferno(length(breaks)), border_color = NA,
gaps_col = (which(!tmp_Splice_GO_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$PG %>% duplicated())-1)[-1],
breaks = breaks , show_colnames = F , treeheight_row = 0, fontsize_row = 8)
Spliceosome abundance across groups with SF3B1 mutational status color coded
left_join(splice_genes_mean,
wideFormat(multiomics_MAE["SF3B1", , "SNPs"], colDataCols = "PG") %>% as_tibble(),
by=c("name"="primary")) %>%
filter(!is.na(PG)) %>%
ggplot(aes( PG, value ))+
geom_boxplot(aes(fill=PG)) + geom_beeswarm(aes(color=as.factor(SNPs_SF3B1))) +
stat_compare_means(method = "anova", label.y = 0.22, hjust=0) +
ggtitle("Spliceosome protein abundance") +
pp_sra_nox +
ylab("Mean abundance of spliceosome proteins")+
theme(aspect.ratio=1) +
stat_compare_means(label = "p.signif", method = "t.test",
ref.group = ".all.", label.y = 0.2, hide.ns = TRUE)+
guides(fill=guide_legend(title="PG"))+
scale_fill_manual(values = colors_CCP) +
scale_color_manual(values = c("grey", "red"))## harmonizing input:
## removing 510 sampleMap rows not in names(experiments)
## removing 1 colData rownames not in sampleMap 'primary'
BCAA
KEGG Valine, leucine and isoleucine degradation
BCAA_genes_mean <- assay(multiomics_MAE[BCAA_genes, ,"proteomics"]) %>% colMeans(na.rm = TRUE) %>% enframe()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'BCAA_CCP_P_plot <- left_join(BCAA_genes_mean,
colData(multiomics_MAE) %>% as_tibble() %>% select(name=patient_ID, PG ),
by=c("name")) %>%
filter(!is.na(PG)) %>%
ggplot(aes( PG, value ))+
geom_boxplot(aes(fill=PG)) + geom_beeswarm() +
ggtitle("BCAA protein abundance") +
pp_sra +
ylab("Mean abundance of BCAA proteins")+
#stat_compare_means(label = "p.signif", method = "t.test",
# ref.group = ".all.", label.y = 0.55, hide.ns = TRUE)+
guides(fill=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP)
BCAA_CCP_P_plot + theme(aspect.ratio=1) +
stat_compare_means(method = "anova", label.y = 0.57, hjust=0)
Heatmap Selected proteins
some_BCAA <- c("ACAT1", "PCCA", "PCCB", "HADH", "HADHB", "HADHA", "HIBADH", "MCCC1", "MCCC2", "MLYCD")
tmp_BCAA <- wideFormat(multiomics_MAE[some_BCAA, ,"proteomics"], colDataCols = c("PG", "IGHV_mutated" )) %>%
as_tibble()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'tmp_BCAA_mx <- tmp_BCAA %>% dplyr::select(-primary, -PG, -IGHV_mutated) %>% as.matrix()
rownames(tmp_BCAA_mx) <- tmp_BCAA$primary
colnames(tmp_BCAA_mx) <- gsub("proteomics_", "", colnames(tmp_BCAA_mx))
tmp_BCAA_anno <- tmp_BCAA[, c("PG", "IGHV_mutated")] %>% as.data.frame()
rownames(tmp_BCAA_anno) <- tmp_BCAA$primary
tmp_BCAA_anno$PG <- factor(tmp_BCAA_anno$PG,levels = c(5,1:4,6))
tmp_BCAA_anno$IGHV[tmp_BCAA_anno$IGHV_mutated==0] <- "U-CLL"
tmp_BCAA_anno$IGHV[tmp_BCAA_anno$IGHV_mutated==1] <- "M-CLL"
tmp_BCAA_anno <- tmp_BCAA_anno %>% select(-IGHV_mutated)
breaks= seq(min(tmp_BCAA_mx), max(tmp_BCAA_mx), 0.1)^2
breaks= sort(c(-breaks, breaks))
breaks <- breaks[! (breaks < min(tmp_BCAA_mx) | breaks > max(tmp_BCAA_mx) )]
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ),
IGHV=c("U-CLL"= "#0571b0", "M-CLL"= "#ca0020"))
pat_order_hclust_splice <- sapply(c(5,1:4,6), function(P){
hc <- hclust(dist(tmp_BCAA_mx[rownames(tmp_BCAA_anno[tmp_BCAA_anno$PG==P,]), ] ))
hc$labels[hc$order]
}) %>% unlist
PG_BCAA_proteins_pheatmap <- pheatmap(t(tmp_BCAA_mx[pat_order_hclust_splice, ]),
annotation_col = tmp_BCAA_anno, annotation_colors = ann_colors, scale = "row", cluster_cols = FALSE,
color = inferno(length(breaks)), border_color = NA,
gaps_col = (which(!tmp_BCAA_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$PG %>% duplicated())-1)[-1],
breaks = breaks , show_colnames = F , treeheight_row = 0, fontsize_row = 5)
Proteasome
KEGG Proteoasome
proteasome_genes_mean <- assay(multiomics_MAE[proteasome_genes, ,"proteomics"]) %>% colMeans(na.rm = TRUE) %>% enframe()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'proteasome_CCP_P_plot <- left_join(proteasome_genes_mean,
colData(multiomics_MAE) %>% as_tibble() %>% select(name=patient_ID, PG ),
by=c("name")) %>%
filter(!is.na(PG)) %>%
ggplot(aes( PG, value ))+
geom_boxplot(aes(fill=PG)) + geom_beeswarm() +
ggtitle("Proteasome protein abundance") +
pp_sra +
ylab("Mean abundance of proteasomal proteins")+
#stat_compare_means(label = "p.signif", method = "t.test",
# ref.group = ".all.", label.y = 0.3, hide.ns = TRUE)+
guides(fill=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP)
proteasome_CCP_P_plot + theme(aspect.ratio=1) +
stat_compare_means(method = "anova", label.y = 0.32, hjust=0)
Heatmap all proteasome proteins
some_proteasome <- proteasome_genes
tmp_proteasome <- wideFormat(multiomics_MAE[some_proteasome, ,"proteomics"], colDataCols = c("PG", "IGHV_mutated" )) %>%
as_tibble()## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'tmp_proteasome_mx <- tmp_proteasome %>% dplyr::select(-primary, -PG, -IGHV_mutated) %>% as.matrix()
rownames(tmp_proteasome_mx) <- tmp_proteasome$primary
colnames(tmp_proteasome_mx) <- gsub("proteomics_", "", colnames(tmp_proteasome_mx))
tmp_proteasome_anno <- tmp_proteasome[, c("PG", "IGHV_mutated")] %>% as.data.frame()
rownames(tmp_proteasome_anno) <- tmp_proteasome$primary
tmp_proteasome_anno$PG <- factor(tmp_proteasome_anno$PG,levels = c(5,1:4,6))
tmp_proteasome_anno$IGHV[tmp_proteasome_anno$IGHV_mutated==0] <- "U-CLL"
tmp_proteasome_anno$IGHV[tmp_proteasome_anno$IGHV_mutated==1] <- "M-CLL"
tmp_proteasome_anno <- tmp_proteasome_anno %>% select(-IGHV_mutated)
breaks= seq(min(tmp_proteasome_mx), max(tmp_proteasome_mx), 0.1)^2
breaks= sort(c(-breaks, breaks))
breaks <- breaks[! (breaks < min(tmp_proteasome_mx) | breaks > max(tmp_proteasome_mx) )]
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ),
IGHV=c("U-CLL"= "#0571b0", "M-CLL"= "#ca0020"))
pat_order_hclust_splice <- sapply(c(5,1:4,6), function(P){
hc <- hclust(dist(tmp_proteasome_mx[rownames(tmp_proteasome_anno[tmp_proteasome_anno$PG==P,]), ] ))
hc$labels[hc$order]
}) %>% unlist
PG_proteasome_proteins_pheatmap <- pheatmap(t(tmp_proteasome_mx[pat_order_hclust_splice, ]),
annotation_col = tmp_proteasome_anno, annotation_colors = ann_colors, scale = "row", cluster_cols = FALSE,
color = inferno(length(breaks)), border_color = NA,
gaps_col = (which(!tmp_proteasome_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$PG %>% duplicated())-1)[-1],
breaks = breaks , show_colnames = F , treeheight_row = 0, fontsize_row = 5)
PCA
prot_pca <- prcomp(t( assay(multiomics_MAE[prot_few_nas , ,"proteomics"]) ))## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'summary(prot_pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 7.8245 6.66337 6.05805 5.39307 4.5962 4.47779 4.28476
## Proportion of Variance 0.1066 0.07734 0.06392 0.05066 0.0368 0.03492 0.03198
## Cumulative Proportion 0.1066 0.18398 0.24790 0.29856 0.3354 0.37028 0.40226
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 4.07217 3.96780 3.89012 3.60729 3.50148 3.39828 3.33271
## Proportion of Variance 0.02888 0.02742 0.02636 0.02267 0.02136 0.02011 0.01935
## Cumulative Proportion 0.43114 0.45856 0.48492 0.50759 0.52894 0.54906 0.56840
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 3.26312 3.19537 3.11575 2.9922 2.91051 2.8649 2.82250
## Proportion of Variance 0.01855 0.01778 0.01691 0.0156 0.01475 0.0143 0.01388
## Cumulative Proportion 0.58695 0.60473 0.62164 0.6372 0.65199 0.6663 0.68017
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 2.7216 2.62868 2.61514 2.56747 2.53808 2.47949 2.4670
## Proportion of Variance 0.0129 0.01204 0.01191 0.01148 0.01122 0.01071 0.0106
## Cumulative Proportion 0.6931 0.70510 0.71702 0.72850 0.73972 0.75043 0.7610
## PC29 PC30 PC31 PC32 PC33 PC34 PC35
## Standard deviation 2.42571 2.36529 2.34684 2.29926 2.28396 2.22802 2.19890
## Proportion of Variance 0.01025 0.00974 0.00959 0.00921 0.00909 0.00865 0.00842
## Cumulative Proportion 0.77128 0.78102 0.79061 0.79982 0.80891 0.81755 0.82598
## PC36 PC37 PC38 PC39 PC40 PC41 PC42
## Standard deviation 2.15073 2.13611 2.08023 2.05930 2.03908 2.01421 1.99135
## Proportion of Variance 0.00806 0.00795 0.00754 0.00739 0.00724 0.00707 0.00691
## Cumulative Proportion 0.83403 0.84198 0.84952 0.85690 0.86415 0.87121 0.87812
## PC43 PC44 PC45 PC46 PC47 PC48 PC49
## Standard deviation 1.97767 1.9325 1.90702 1.86844 1.84392 1.83474 1.81373
## Proportion of Variance 0.00681 0.0065 0.00633 0.00608 0.00592 0.00586 0.00573
## Cumulative Proportion 0.88493 0.8914 0.89777 0.90385 0.90978 0.91564 0.92137
## PC50 PC51 PC52 PC53 PC54 PC55 PC56
## Standard deviation 1.77205 1.76322 1.74185 1.72077 1.70172 1.6595 1.63090
## Proportion of Variance 0.00547 0.00542 0.00528 0.00516 0.00504 0.0048 0.00463
## Cumulative Proportion 0.92684 0.93225 0.93754 0.94270 0.94774 0.9525 0.95717
## PC57 PC58 PC59 PC60 PC61 PC62 PC63
## Standard deviation 1.61852 1.59146 1.56141 1.53137 1.5157 1.49844 1.46325
## Proportion of Variance 0.00456 0.00441 0.00425 0.00408 0.0040 0.00391 0.00373
## Cumulative Proportion 0.96173 0.96614 0.97039 0.97447 0.9785 0.98239 0.98612
## PC64 PC65 PC66 PC67 PC68
## Standard deviation 1.44810 1.43129 1.39002 1.3758 6.207e-15
## Proportion of Variance 0.00365 0.00357 0.00337 0.0033 0.000e+00
## Cumulative Proportion 0.98977 0.99334 0.99670 1.0000 1.000e+00prot_pca_x <- as_tibble(prot_pca$x[,1:10])
prot_pca_x$pat_ID <- colnames(assay(multiomics_MAE[prot_few_nas , ,"proteomics"]))## harmonizing input:
## removing 522 sampleMap rows not in names(experiments)
## removing 13 colData rownames not in sampleMap 'primary'prot_pca_x <- left_join(prot_pca_x,
wideFormat(multiomics_MAE[, ,c("SNPs","chrom_abber", "health_record_bin") ],
colDataCols = c("gender", "treatment_status", "PG")) %>% as_tibble() ,
by=c("pat_ID"="primary") )## harmonizing input:
## removing 348 sampleMap rows not in names(experiments)#prot_pca_x <- prot_pca_x %>% replace(is.na(.), "unknown")
ggplot(prot_pca_x, aes(PC1, PC2)) +
geom_point(aes(color=as.factor(health_record_bin_IGHV_mutated), shape=as.factor(chrom_abber_trisomy12))) + pp_sra +
scale_shape_manual(values = c( 16, 1, 4)) +
scale_color_manual(values = c( "#0571b0","#ca0020", "grey")) + theme(aspect.ratio = 1)## Warning: Removed 4 rows containing missing values (geom_point).
ggplot(prot_pca_x, aes(PC1, PC3)) +geom_point(aes(color=as.factor(health_record_bin_IGHV_mutated), shape=as.factor(chrom_abber_trisomy12)) ) +
scale_shape_manual(values = c( 16, 1, 4)) +
scale_color_manual(values = c( "#0571b0","#ca0020", "grey"))+ pp_sra+ theme(aspect.ratio = 1)## Warning: Removed 4 rows containing missing values (geom_point).
ggplot(prot_pca_x, aes(PC2, PC3)) +geom_point(aes(color=as.factor(health_record_bin_IGHV_mutated), shape=as.factor(chrom_abber_trisomy12)) ) +
scale_shape_manual(values = c( 16, 1, 4)) +
scale_color_manual(values = c( "#0571b0", "#ca0020", "grey")) + pp_sra+ theme(aspect.ratio = 1)## Warning: Removed 4 rows containing missing values (geom_point).
ggplot(prot_pca_x, aes(PC1, PC2)) +geom_point(aes(color=gender)) + pp_sra + scale_color_manual(values = c( "#0571b0","#ca0020", "grey")) + theme(aspect.ratio = 1)
PCA_CCP_1_2 <- ggplot(prot_pca_x, aes(PC1, PC2)) +
geom_point(aes(fill=PG),shape=21, color="grey") + pp_sra +
guides(color=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP) +
xlab( paste0("PC1 (", round(summary(prot_pca)$importance[2,1], 2)*100, "%)" ))+
ylab( paste0("PC2 (", round(summary(prot_pca)$importance[2,2], 2)*100, "%)" ))
PCA_CCP_1_2 +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
PCA_CCP_1_3 <- ggplot(prot_pca_x, aes(PC1, PC3)) +
geom_point(aes(fill=PG),shape=21, color="grey") + pp_sra +
guides(color=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP) +
xlab( paste0("PC1 (", round(summary(prot_pca)$importance[2,1], 2)*100, "%)" ))+
ylab( paste0("PC3 (", round(summary(prot_pca)$importance[2,3], 2)*100, "%)" ))
PCA_CCP_1_3 +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
PCA_CCP_2_3 <- ggplot(prot_pca_x, aes(PC2, PC3)) +
geom_point(aes(fill=PG),shape=21, color="grey") + pp_sra +
guides(color=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP) +
xlab( paste0("PC2 (", round(summary(prot_pca)$importance[2,2], 2)*100, "%)" ))+
ylab( paste0("PC3 (", round(summary(prot_pca)$importance[2,3], 2)*100, "%)" ))
PCA_CCP_2_3 +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
PCA_CCP_1_4 <- ggplot(prot_pca_x, aes(PC1, PC4)) +
geom_point(aes(fill=PG),shape=21, color="grey") + pp_sra +
guides(color=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP) +
xlab( paste0("PC1 (", round(summary(prot_pca)$importance[2,1], 2)*100, "%)" ))+
ylab( paste0("PC4 (", round(summary(prot_pca)$importance[2,4], 2)*100, "%)" ))
PCA_CCP_1_4 +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
tsne RNA
genes_no_nas <- multiomics_MAE[["RNAseq_norm"]] %>% is.na() %>% rowSums()
genes_no_nas <- genes_no_nas[ genes_no_nas == 0 ] %>% names()
set.seed(10)
rtsne_out_RNA <- Rtsne( t(assay(multiomics_MAE[genes_no_nas , ,"RNAseq_norm"])), perplexity = 10 )
rtsne_out_RNA_df <- rtsne_out_RNA$Y %>% as.data.frame() %>% as_tibble()
rtsne_out_RNA_df$pat_ID <- colnames(assay(multiomics_MAE[genes_no_nas , ,"RNAseq_norm"]))
rtsne_out_RNA_df <- left_join(rtsne_out_RNA_df,
(wideFormat(multiomics_MAE[, ,c("SNPs","chrom_abber", "health_record_bin") ], colDataCols = c("gender", "treatment_status", "doehner_groups", "PG", "CCP6_RNA")) %>% as_tibble()),
by=c("pat_ID"="primary") )
rtsne_out_RNA_df <- mutate_at(rtsne_out_RNA_df, colnames(rtsne_out_RNA_df %>% dplyr::select(SNPs_ATM:health_record_bin_treated)), as.logical)
#rtsne_out_RNA_df <- rtsne_out_RNA_df %>% replace(is.na(.), "unknown")
tsne_CCP_R_plot <- ggplot(rtsne_out_RNA_df, aes(V1, V2)) +
geom_point(aes(fill=PG), color="grey", shape=21) +
scale_fill_manual(values = colors_CCP)+
pp_sra +
guides(color=guide_legend(title="PG"))
tsne_CCP_R_plot + theme(aspect.ratio=1, plot.title = element_text(size = 30))
tsne_CCPRNA_R_plot <- ggplot(rtsne_out_RNA_df, aes(V1, V2)) +
geom_point(aes(fill=CCP6_RNA), color="grey", shape=21) +
scale_color_hue()+
pp_sra +
guides(color=guide_legend(title="CC_RNA"))
tsne_CCPRNA_R_plot+ theme(aspect.ratio=1, plot.title = element_text(size = 30))
sapply(colnames(rtsne_out_RNA_df)[c(4,5,9:27)], function(var){
print(ggplot(rtsne_out_RNA_df, aes(V1, V2)) +
geom_point(aes_string(color=var), size=3) +
scale_color_manual(values = c("#92c5de", "#f4a582", "grey")) +
ggtitle(gsub("SNPs_", "", gsub("chrom_abber_", "", gsub( "health_record_bin_", "", var) ))) +
pp_sra_noguides +
theme(aspect.ratio=1, plot.title = element_text(size = 30)) )
})
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing missing values (geom_point).
## gender treatment_status SNPs_ATM SNPs_BIRC3 SNPs_EGR2 SNPs_NOTCH1
## data List,27 List,27 List,27 List,27 List,27 List,27
## layers List,1 List,1 List,1 List,1 List,1 List,1
## scales ? ? ? ? ? ?
## mapping List,2 List,2 List,2 List,2 List,2 List,2
## theme List,93 List,93 List,93 List,93 List,93 List,93
## coordinates ? ? ? ? ? ?
## facet ? ? ? ? ? ?
## plot_env ? ? ? ? ? ?
## labels List,4 List,4 List,4 List,4 List,4 List,4
## guides List,4 List,4 List,4 List,4 List,4 List,4
## SNPs_POT1 SNPs_SF3B1 SNPs_TP53 SNPs_XPO1 chrom_abber_del11q
## data List,27 List,27 List,27 List,27 List,27
## layers List,1 List,1 List,1 List,1 List,1
## scales ? ? ? ? ?
## mapping List,2 List,2 List,2 List,2 List,2
## theme List,93 List,93 List,93 List,93 List,93
## coordinates ? ? ? ? ?
## facet ? ? ? ? ?
## plot_env ? ? ? ? ?
## labels List,4 List,4 List,4 List,4 List,4
## guides List,4 List,4 List,4 List,4 List,4
## chrom_abber_del13q14 chrom_abber_del17p13 chrom_abber_gain8q24
## data List,27 List,27 List,27
## layers List,1 List,1 List,1
## scales ? ? ?
## mapping List,2 List,2 List,2
## theme List,93 List,93 List,93
## coordinates ? ? ?
## facet ? ? ?
## plot_env ? ? ?
## labels List,4 List,4 List,4
## guides List,4 List,4 List,4
## chrom_abber_trisomy12 health_record_bin_elderly_at_diagnosis
## data List,27 List,27
## layers List,1 List,1
## scales ? ?
## mapping List,2 List,2
## theme List,93 List,93
## coordinates ? ?
## facet ? ?
## plot_env ? ?
## labels List,4 List,4
## guides List,4 List,4
## health_record_bin_elderly_patient health_record_bin_gender_binary
## data List,27 List,27
## layers List,1 List,1
## scales ? ?
## mapping List,2 List,2
## theme List,93 List,93
## coordinates ? ?
## facet ? ?
## plot_env ? ?
## labels List,4 List,4
## guides List,4 List,4
## health_record_bin_IGHV_mutated
## data List,27
## layers List,1
## scales ?
## mapping List,2
## theme List,93
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
## health_record_bin_komplex_abberant_karyotype
## data List,27
## layers List,1
## scales ?
## mapping List,2
## theme List,93
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
## health_record_bin_treated
## data List,27
## layers List,1
## scales ?
## mapping List,2
## theme List,93
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4Color tsne based on expression of B-cell receptor signaling pathways
BCR_genes_RNA <- left_join(enframe(BCR_genes, value = "symbol", name = NULL), metadata(multiomics_MAE)$gene_symbol_mapping, by=c("symbol"="hgnc_symbol")) %>%
filter(!is.na(ensembl_gene_id)) %>% .$ensembl_gene_id
BCR_genes_RNA_mean <- assay(multiomics_MAE[BCR_genes_RNA, ,"RNAseq_norm"]) %>% colMeans(na.rm = TRUE) %>% enframe()Save important plots
save(tsne_CCP_P_plot, tsne_CCP_R_plot,tsne_CCPRNA_R_plot, tsne_IGHVtris_plot, BCR_CCP_P_plot, splicing_CCP_P_plot,
PCA_CCP_1_2, PCA_CCP_1_3, PCA_CCP_1_4, PCA_CCP_2_3,
PG_BCR_proteins_pheatmap,
PG_splice_proteins_pheatmap, PG_BCAA_proteins_pheatmap,
BCAA_CCP_P_plot, proteasome_CCP_P_plot, PG_proteasome_proteins_pheatmap,
file = "RData_plots/CLL_Proteomics_DimensionReduction_Plots.RData")Session Info
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] viridis_0.5.1 viridisLite_0.3.0
## [3] gplots_3.1.1 MultiAssayExperiment_1.14.0
## [5] Rtsne_0.15 limma_3.44.3
## [7] fdrtool_1.2.16 vsn_3.56.0
## [9] forcats_0.5.1 stringr_1.4.0
## [11] dplyr_1.0.3 purrr_0.3.4
## [13] readr_1.4.0 tidyr_1.1.2
## [15] tibble_3.0.6 tidyverse_1.3.0
## [17] DESeq2_1.28.1 SummarizedExperiment_1.18.2
## [19] DelayedArray_0.14.1 matrixStats_0.58.0
## [21] Biobase_2.48.0 GenomicRanges_1.40.0
## [23] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [25] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [27] ggbeeswarm_0.6.0 ggpubr_0.4.0
## [29] Hmisc_4.4-2 ggplot2_3.3.3
## [31] Formula_1.2-4 survival_3.2-7
## [33] lattice_0.20-41 Matrix_1.3-2
## [35] pheatmap_1.0.12 gtools_3.8.2
## [37] plyr_1.8.6
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1 splines_4.0.2
## [4] BiocParallel_1.22.0 digest_0.6.27 htmltools_0.5.1.1
## [7] magrittr_2.0.1 checkmate_2.0.0 memoise_2.0.0
## [10] cluster_2.1.0 openxlsx_4.2.3 annotate_1.66.0
## [13] modelr_0.1.8 jpeg_0.1-8.1 colorspace_2.0-0
## [16] blob_1.2.1 rvest_0.3.6 haven_2.3.1
## [19] xfun_0.20 crayon_1.4.0 RCurl_1.98-1.2
## [22] jsonlite_1.7.2 genefilter_1.70.0 glue_1.4.2
## [25] gtable_0.3.0 zlibbioc_1.34.0 XVector_0.28.0
## [28] car_3.0-10 abind_1.4-5 scales_1.1.1
## [31] DBI_1.1.1 rstatix_0.6.0 Rcpp_1.0.6
## [34] xtable_1.8-4 htmlTable_2.1.0 foreign_0.8-81
## [37] bit_4.0.4 preprocessCore_1.50.0 htmlwidgets_1.5.3
## [40] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.1
## [43] farver_2.0.3 pkgconfig_2.0.3 XML_3.99-0.5
## [46] nnet_7.3-15 dbplyr_2.0.0 locfit_1.5-9.4
## [49] labeling_0.4.2 tidyselect_1.1.0 rlang_0.4.10
## [52] AnnotationDbi_1.50.3 munsell_0.5.0 cellranger_1.1.0
## [55] tools_4.0.2 cachem_1.0.1 cli_2.3.0
## [58] generics_0.1.0 RSQLite_2.2.3 broom_0.7.4
## [61] evaluate_0.14 fastmap_1.1.0 yaml_2.2.1
## [64] knitr_1.31 bit64_4.0.5 fs_1.5.0
## [67] zip_2.1.1 caTools_1.18.1 xml2_1.3.2
## [70] compiler_4.0.2 rstudioapi_0.13 beeswarm_0.2.3
## [73] curl_4.3 png_0.1-7 affyio_1.58.0
## [76] ggsignif_0.6.0 reprex_1.0.0 geneplotter_1.66.0
## [79] stringi_1.5.3 highr_0.8 vctrs_0.3.6
## [82] pillar_1.4.7 lifecycle_0.2.0 BiocManager_1.30.10
## [85] data.table_1.13.6 bitops_1.0-6 R6_2.5.0
## [88] latticeExtra_0.6-29 affy_1.66.0 KernSmooth_2.23-18
## [91] gridExtra_2.3 rio_0.5.16 vipor_0.4.5
## [94] assertthat_0.2.1 withr_2.4.1 GenomeInfoDbData_1.2.3
## [97] hms_1.0.0 grid_4.0.2 rpart_4.1-15
## [100] rmarkdown_2.6 carData_3.0-4 lubridate_1.7.9.2
## [103] base64enc_0.1-3